A right-turn vehicle gap selection behavior analysis method based on electric bicycles

Abstract

This invention discloses a method for analyzing the gap selection behavior of right-turning vehicles based on electric bicycles. Based on survey data, the conflicting gap selection behavior of right-turning motor vehicles and electric bicycles is analyzed in detail. First, the intersection geometry conditions obtained from the survey are used to determine the conflict area between electric bicycles and right-turning motor vehicles. Gap types T and G are defined, and gaps at different survey locations are statistically analyzed. Subsequently, gaps are grouped in 0.1s units, and the number of accepted and rejected gaps within each group of different gap types is counted, and the gap acceptance probability of different types and groups is calculated. Finally, the Weibull distribution is selected as the fitting function, and the model parameters are calibrated using statistical data. A method for analyzing the gap selection behavior of right-turning vehicles based on the operating characteristics of electric bicycles is established. This method can realistically reproduce the operating characteristics of right-turning vehicles, improve traffic simulation models, and be used for safety assessment of conflict behavior at signalized intersections.

Description

Technical Field

[0001] The invention belongs to the technical field of traffic management and control, and in particular relates to a right-turn vehicle gap selection behavior analysis method based on an electric bicycle. Background Art

[0002] Intersections, as traffic nodes on urban roads, bring together different travel modes such as motor vehicles, non-motor vehicles, and pedestrians. The complexity of their traffic flow is much higher than that of ordinary roads, and safety issues are increasingly attracting people's attention. Among them, vehicle behavior analysis is an important means of road safety research. By implementing traffic control at intersections based on the characteristics of vehicle behavior, corresponding control methods can be proposed in a targeted manner, thereby improving the traffic efficiency and safety of intersections.

[0003] Currently, most signalized intersections in my country use right-turn permit phase control. Compared to left-turning vehicles, right-turning vehicles are more susceptible to interference from other traffic participants, leading to serious conflicts between vehicles and non-vehicles. As people's demands for travel quality gradually increase, traditional bicycles, which used to be the mainstay, can no longer meet their demands for lightweight and energy-efficient travel. Due to their speed and accessibility, electric bicycles have gradually replaced traditional bicycles as the primary means of transportation for residents in some cities in my country. Therefore, research on the behavior analysis of right-turning vehicles at signalized intersections considering the influence of electric bicycles is of practical significance.

[0004] Existing intersection safety assessment methods primarily involve two approaches: ex post safety assessment based on traffic accident data, and ex ante safety assessment based on empirical data, primarily traffic conflict analysis. Ex post safety assessments must be conducted after improved traffic management measures have been implemented, resulting in significant limitations. Traffic simulation software has emerged to address this issue, but existing simulation software often simplifies traffic flows, making safety assessments unreliable. Summary of the Invention

[0005] The present invention aims to overcome the shortcomings of the existing technology by providing a method for analyzing the gap selection behavior of right-turning vehicles, taking into account the operating characteristics of electric bicycles. This method focuses on analyzing the motion characteristics of right-turning vehicles and electric bicycles near the point of conflict. The method then classifies gaps into different types based on the operating characteristics of electric bicycles. A cumulative Weibull distribution is used to fit the gap acceptance probability, analyzing the relationship between right-turn vehicle decision-making behavior and gap size and type. This proposed method can be used as part of a micro-behavior simulation analysis method, enabling realistic reproduction of traffic behavior and possessing significant significance in traffic safety assessments.

[0006] The purpose of the present invention is achieved through the following technical solutions:

[0007] A method for analyzing right-turn vehicle gap selection behavior based on an electric bicycle comprises the following steps:

[0008] S1. Select a clear day with wind speeds less than level 5 during the morning rush hour between 8:00 and 8:30 AM on a weekday. Use manual measurement to determine the geometric conditions of the survey site and use aerial photography to capture video of several signalized intersections.

[0009] S2. Set the analysis period to the end of the green light on the through phase of the target entrance lane. Use Track Pro software to extract the trajectory, speed, flow rate, acceleration of right-turning vehicles, and the trajectory, speed, and time when the e-bike passes the conflict boundary.

[0010] S3. Determine the conflict impact area between right-turning vehicles and electric bicycles;

[0011] S4. Count the time it takes for a single electric bicycle to reach the boundary of the conflict zone and the time gap between two consecutive electric bicycles passing through the conflict zone, defining them as time gap T and time gap G, respectively.

[0012] S5. Assuming that the right-turning vehicle approaches the electric bicycle lane as the right-turning vehicle decision moment, the location is the decision point, by tracking the running state of the right-turning vehicle near the conflict zone, extracting the time gap T and time gap G data;

[0013] S6. Process the time gap T and time gap G data to obtain the number of right-turn vehicles accepting time gap T and time gap G and rejecting time gap T and time gap G at different survey locations;

[0014] S7. The time gap T and time gap G are grouped in units of 0.1s, and the number of accepted and rejected time gaps in each group is counted for different time gap types, and the time gap acceptance probability of different groups of different time gap types is calculated using the formula;

[0015] S8. Select Weibull distribution as the fitting function, draw the acceptance time gap probability distribution curve, and calibrate the fitting function parameters to obtain a right-turn vehicle gap selection behavior analysis method based on the operating characteristics of electric bicycles.

[0016] Furthermore, the geometric conditions described in step S1 include the intersection angle θ, in rad; the intersection turning radius R, in m; the width L of the non-motorized vehicle lane, in m; the intersection r of the center line of the exit lane and the center line of the entrance lane; and the distance C between the center line of the entrance lane and the inner edge line of the sidewalk of the exit lane, in m.

[0017] Furthermore, the conflict impact area described in step S3 is defined as the area where the electric bicycle passage area overlaps with the right-turning vehicle trajectory, W is the vehicle body width, and based on the intersection geometry conditions obtained in step S1, the right-turning vehicle trajectory model is used to determine the conflict zone CZ between the right-turning vehicle and the electric bicycle.

[0018] Furthermore, the time gap T is the time it takes for a single electric bicycle to reach the boundary of the conflict area, and the time gap G is the time it takes for two consecutive electric bicycles to pass through the conflict area, that is, the time difference between the first electric bicycle leaving the conflict area and the second electric bicycle arriving at the boundary of the conflict area.

[0019] Furthermore, in step S5, by tracking the running status of the right-turning vehicle approaching the conflict zone, the number of accepted time slots and rejected time slots is extracted, wherein the data of accepted time slots is collected when not stopping; the data of rejected time slots is collected when stopping; and the data of accepted time slots is collected when starting to pass again.

[0020] Furthermore, the processing of the time interval T data and the time interval G data includes: (1) extracting only the data of the leading vehicle among the right-turning vehicles in the queue to exclude the influence of the following behavior on the decision-making; (2) extracting only the data of small passenger cars without considering the influence of the vehicle model; (3) eliminating the data of the right-turning vehicles traveling side by side or being interfered with by other vehicles during the turning process.

[0021] Furthermore, the gap acceptance probability is shown in formula (1), P(x) i,TorG The probability value of group i accepting time gap T or time gap G, N i is the number of group i;

[0022]

[0023] Furthermore, formula (2) represents the cumulative Weibull distribution function with two parameters, the scale parameter α and the shape parameter β;

[0024]

[0025] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for analyzing the right-turn vehicle gap selection behavior based on an electric bicycle are implemented.

[0026] The present invention also provides an application of a right-turn vehicle gap selection behavior analysis method based on an electric bicycle. The right-turn vehicle gap selection behavior analysis method is used to truly reproduce the operating characteristics of right-turn vehicles, improve the traffic simulation model, and be used for safety assessment of conflict behaviors at signalized intersections.

[0027] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0028] 1. Most of the existing research on vehicle-non-motor vehicle conflicts considers electric bicycles together with traditional bicycles as non-motor vehicles, and the research mainly focuses on aspects such as arrival characteristics and release characteristics. Since the proportion of electric bicycles has far exceeded that of traditional bicycles in some cities in my country, becoming an important means of transportation for residents, and compared with traditional bicycles, they are faster and more maneuverable, it is of great significance for the present invention to study the safety characteristics of electric bicycles as an independent group.

[0029] 2. As part of the research on intersection conflict mechanisms, the method of the present invention aims to study the degree of acceptance of right-turning vehicles for the clearance of electric bicycles, and analyze the severity of the conflict between the two in combination with the vehicle behavior at the conflict point. Existing simulation software will simplify traffic flow to a certain extent, making safety evaluation unreliable. Turning vehicles are the main cause of conflicts at signalized intersections. By incorporating the research results of the present invention into existing traffic simulation models, the operating characteristics of vehicles can be realistically reproduced and used for safety assessment of conflict behavior at signalized intersections. This is the premise and foundation for formulating traffic control plans and improving the operating efficiency and safety of intersections. It can also provide a comprehensive and accurate theoretical basis for the spatiotemporal optimization of intersections, thereby improving the operating efficiency of intersections.

[0030] 3. Previous studies on intersection gap selection behavior methods have mostly focused on evaluating intersection capacity, rarely considering the crossing behavior of right-turning vehicles, e-bikes, or pedestrians, and the impact of intersection geometry on crossing decisions. The present invention comprehensively considers intersection geometry during the analysis process, determines the conflict area, and calculates the gap acceptance probability. As part of our team's series of studies on right-turn vehicle conflict behavior, this invention helps to better reproduce the traffic behavior of right-turning vehicles at intersections. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of the process of the present invention;

[0032] Figure 2 Define schematic diagrams for intersection geometry parameters;

[0033] Figure 3a and Figure 3b Schematic diagrams for defining conflict areas and time gaps respectively;

[0034] Figure 4a and Figure 4b Schematic diagrams are defined for time gap T and time gap G respectively.

[0035] Figure 5Calculate the value of the acceptance gap probability and fit the cumulative Weibull distribution curve. DETAILED DESCRIPTION

[0036] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0037] This embodiment proposes a method for analyzing the gap selection behavior of right-turning vehicles based on the operating characteristics of electric bicycles. According to the intersection geometry and traffic flow data, a gap acceptance probability model is established using the Weibull distribution. Figure 1 , the method steps of the present invention are as follows:

[0038] Step S1. Select a working day with morning rush hour, clear weather and wind speed less than level 5, and use manual measurement to collect the geometric conditions of the survey site, such as Figure 2 As shown, the geometric conditions include: θ is the intersection angle (rad); R is the intersection turning radius (m); L is the width of the non-motorized vehicle lane (m); r is the intersection point of the exit and entrance centerlines; and C is the distance between the centerline of the rightmost entrance lane and the inner edge of the exit sidewalk (m). A video acquisition method was also used to obtain videos from high-angle aerial photography of multiple signalized intersections.

[0039] Step S2. Using Track Pro software, extract traffic flow data, including the trajectory, speed, flow rate, and acceleration of right-turning vehicles at the end of the green light on the through phase of the target entrance lane, as well as the trajectory, speed, and time of crossing the conflict boundary of electric bicycles;

[0040] The analysis period is at the end of the green light during the through phase of the target entrance lane. This is because right-turning vehicles tend to have a higher concentration of trajectories at the end of the green light, primarily avoiding collisions with electric bicycles by changing their speed. This can be reproduced using a specific model. Trackpro software analyzes spatiotemporal data of pedestrians and vehicles using video acquisition technology. Pixel coordinates can be converted to geographic coordinates through software parameter settings. This software is widely used to collect vehicle trajectory, speed, acceleration, and other data.

[0041] Step S3: Determine the conflict impact area between right-turning vehicles and electric bicycles. Figure 3a As shown in Figure 1, the conflict zone is defined as the area where the electric bicycle passage area overlaps with the right-turning vehicle trajectory. W is the vehicle body width. Combined with the intersection geometric conditions obtained in step S1, the right-turning vehicle trajectory model (Qu Zhaowei, Luo Ruiqi, Chen Yongheng, et al. Trajectory characteristics of right-turning motor vehicles at signalized intersections [J]. Journal of Zhejiang University: Engineering Edition, 2018.) can be used to determine the conflict zone CZ between the right-turning vehicle and the electric bicycle.

[0042] Step S4. Count the time it takes for a single electric bicycle to reach the boundary of the conflict area and the time gap between two consecutive electric bicycles passing through the conflict area, which are defined as T (time) and G (gap) respectively; Figure 4a and Figure 4b As shown, T(time) is the time it takes for a single e-bike to reach the boundary of the conflict zone, and G(gap) is the time it takes for two consecutive e-bikes to pass through the conflict zone (the difference between the time the first e-bike exits the conflict zone and the time the second e-bike reaches the boundary). Since all potential conflicts with pedestrians occur within the conflict zone, the time gap T and time gap G are precisely defined by excluding the time it takes for pedestrians to clear the area occupied by their vehicles. When and where the driver makes this judgment and decides whether to accept or reject it depends on the driver's characteristics and the road environment. The number of time gaps T and time gaps G depends on the demand and random characteristics of e-bikes.

[0043] Assuming that the moment a right-turning vehicle approaches an e-bike lane is the right-turn decision moment, and its location is the decision point, the operating status of right-turning vehicles approaching the conflict zone is tracked, and the acceptance time gap T and time gap G data, as well as the rejection time gap T and time gap G data, are extracted. The data is processed, as shown in Table 1.

[0044] Table 1 Number of right-turn vehicles accepting T and G and rejecting T and G at different survey locations

[0045]

[0046] The decision position and time for a right-turning vehicle to make a crossable gap are related to many factors, such as the driver's characteristics, intersection layout, speed and operating characteristics of the electric bicycle. Therefore, it is difficult to determine the decision point. In this embodiment, it is assumed that the decision moment of the right-turning vehicle is when the right-turning vehicle approaches the electric bicycle lane, and the location at which the right-turning vehicle makes the decision is the decision point.

[0047] By tracking the running status of right-turning vehicles approaching the conflict zone, the number of accepted time gaps and rejected time gaps is extracted (without stopping, collecting the accepted time gaps; stopping, collecting the rejected time gaps; starting again and passing, collecting the accepted time gaps). Some vehicles will miss the rejected gaps and choose the accepted gaps by adjusting their speed. Since it is difficult to determine the decision point when the driver finds the gap and adjusts the speed, this situation is not considered. Figure 3b , the driver slows down after observing electric bicycle No. 1, which is defined as rejection T. The driver chooses to accelerate after electric bicycle No. 1 passes the conflict area and passes before electric bicycle No. 2 reaches the conflict area, which is defined as acceptance G.

[0048] Data processing: (1) Only the data of the first vehicle among the right-turning vehicles passing through the queue are extracted to exclude the influence of the following behavior on the decision-making; (2) Only the data of small right-turning vehicles are extracted, and the influence of the vehicle model on the research method is not considered; (3) The data of right-turning vehicles traveling side by side or being interfered with by other vehicles during the turning process are eliminated.

[0049] Step S5. Group the time gaps T and G into 0.1s units, count the number of accepted and rejected gaps in each group of different gap types, and calculate the gap acceptance probability of different types and groups using formula (1). P(x) i,TorG The probability value of group i accepting time gap T or time gap G, N i is the number of group i.

[0050]

[0051] Step S6. Select Weibull distribution as the fitting function, as shown in formula (2). Draw the acceptance gap probability distribution curve, as shown in Figure 5 The model distribution parameters are calibrated and the fitting results are shown in Table 2.

[0052] In traditional Logit and Probit models, the acceptance probability value can reach zero under certain conditions, which is one of the main drawbacks of using such models. This embodiment uses the cumulative Weibull distribution to fit the gap acceptance probability distribution. The Weibull distribution is a widely used life distribution in reliability engineering (Abernethy, 2004). It is a versatile distribution that can exhibit the characteristics of other types of distributions depending on its parameter values ​​and can also be used to represent the distribution probability of road traffic flow. Formula (2) represents the cumulative Weibull distribution function with two parameters: scale parameter α and shape parameter β.

[0053]

[0054] Table 2 Probability distribution function parameter fitting results

[0055]

[0056] Preferably, the embodiments of the present application further provide a specific implementation of an electronic device capable of implementing all steps of the method for analyzing right-turn vehicle gap selection based on an electric bicycle in the above embodiment. The electronic device specifically includes the following contents:

[0057] Processor, memory, communications interface, and bus;

[0058] Among them, the processor, memory, and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between related devices such as server-side devices, metering devices, and user-side devices.

[0059] The processor is used to call the computer program in the memory, and when the processor executes the computer program, all the steps in the right-turn vehicle gap selection behavior analysis method based on the electric bicycle in the above embodiment are implemented.

[0060] Preferably, an embodiment of the present application also provides a computer-readable storage medium capable of implementing all steps of the method for analyzing the gap selection behavior of right-turning vehicles based on electric bicycles in the above-mentioned embodiment. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all steps of the method for analyzing the gap selection behavior of right-turning vehicles based on electric bicycles in the above-mentioned embodiment are implemented.

[0061] Preferably, the embodiments of the present application also provide the application of a right-turning vehicle gap selection behavior analysis method based on electric bicycles. By studying the acceptance of right-turning vehicles to the electric bicycle gap, combined with the vehicle behavior at the conflict point, the severity of the conflict between the two is analyzed. By combining them with the existing simulation model, the operating characteristics of the vehicle can be truly reproduced, which is used for the safety assessment of conflict behavior at signalized intersections. It is the premise and basis for formulating traffic control plans and improving the operating efficiency and safety of intersections. It can also provide a comprehensive and accurate theoretical basis for the spatiotemporal optimization of intersections, thereby improving the operating efficiency of intersections.

[0062] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] Although the present application provides method operation steps such as embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative work. The order of steps listed in the embodiments is only one way of executing the steps among many steps and does not represent the only execution order. When an actual device or client product is executed, it can be executed in the order shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0064] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0067] The present invention is not limited to the embodiments described above. The above description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention. The above specific embodiments are merely illustrative and not restrictive. Without departing from the scope of the present invention and the scope of protection of the claims, those skilled in the art may make various specific modifications based on the teachings of the present invention, all of which fall within the scope of protection of the present invention.

Claims

1. A method for analyzing the gap selection behavior of right-turning vehicles based on electric bicycles, characterized in that: The steps include: S1. Select a clear day with wind speeds less than level 5 during the morning rush hour between 8:00 and 8:30 AM on a weekday. Use manual measurement to determine the geometric conditions of the survey site and use aerial photography to capture video of several signalized intersections. S2. Set the analysis period to the end of the green light on the through phase of the target entrance lane. Use Track Pro software to extract the trajectory, speed, flow rate, acceleration of right-turning vehicles, and the trajectory, speed, and time when the e-bike passes the conflict boundary. S3. Determine the conflict impact area between right-turning vehicles and electric bicycles; S4. Count the time it takes for a single electric bicycle to reach the boundary of the conflict zone and the time gap between two consecutive electric bicycles passing through the conflict zone, defining them as time gap T and time gap G, respectively. S5. Assuming that the right-turning vehicle approaches the electric bicycle lane as the right-turning vehicle decision moment, the location is the decision point, by tracking the running state of the right-turning vehicle near the conflict zone, extracting the time gap T and time gap G data; S6. Process the time gap T and time gap G data to obtain the number of right-turn vehicles accepting time gap T and time gap G and rejecting time gap T and time gap G at different survey locations; S7. The time gap T and time gap G are grouped in units of 0.1s, and the number of accepted and rejected time gaps in each group is counted for different time gap types, and the probability of acceptance of time gaps of different groups of different time gap types is calculated; S8. Select Weibull distribution as the fitting function, draw the acceptance time gap probability distribution curve, and calibrate the fitting function parameters to obtain a right-turn vehicle gap selection behavior analysis method based on the operating characteristics of electric bicycles.

2. The method for analyzing the right-turn vehicle gap selection behavior based on an electric bicycle according to claim 1, characterized in that: The geometric conditions described in step S1 include the intersection angle θ, in rad; the intersection turning radius R, in m; the width L of the non-motorized vehicle lane, in m; the intersection point r of the centerline of the exit lane and the centerline of the entrance lane; and the distance C between the centerline of the entrance lane and the inner edge of the sidewalk of the exit lane, in m.

3. The method for analyzing the right-turn vehicle gap selection behavior based on an electric bicycle according to claim 1, characterized in that: The conflict impact area described in step S3 is defined as the area where the electric bicycle passage area overlaps with the right-turning vehicle trajectory. W is the vehicle body width. Based on the intersection geometry conditions obtained in step S1, the right-turning vehicle trajectory model is used to determine the conflict zone CZ between the right-turning vehicle and the electric bicycle.

4. The method for analyzing the right-turn vehicle gap selection behavior based on an electric bicycle according to claim 1, characterized in that: The time gap T is the time it takes for a single electric bicycle to reach the boundary of the conflict area, and the time gap G is the time it takes for two consecutive electric bicycles to pass through the conflict area, that is, the time difference between the first electric bicycle leaving the conflict area and the second electric bicycle reaching the boundary of the conflict area.

5. The method for analyzing right-turn vehicle gap selection behavior based on an electric bicycle according to claim 1, characterized in that: In step S5, by tracking the running status of the right-turning vehicle approaching the conflict zone, the number of accepted time slots and rejected time slots is extracted, wherein the data of accepted time slots is collected when the vehicle is not stopping; the data of rejected time slots is collected when the vehicle is stopped; and the data of accepted time slots is collected when the vehicle starts to pass again.

6. The method for analyzing right-turn vehicle gap selection behavior based on an electric bicycle according to claim 1, characterized in that: The processing of the time interval T data and the time interval G data includes: (1) extracting only the data of the leading vehicle among the right-turning vehicles passing through the queue to eliminate the influence of the following behavior on the decision-making; (2) extracting only the data of small passenger cars without considering the influence of the vehicle model; (3) eliminating the data of right-turning vehicles traveling side by side or being interfered with by other vehicles during the turning process.

7. The method for analyzing right-turn vehicle gap selection behavior based on an electric bicycle according to claim 1, characterized in that: The gap acceptance probability is shown in formula (1), The probability value of accepting time gap T or time gap G for group i is, is the number of time slots T or G accepted by the i-th group, is the number of rejected time slots T or time slots G for group i; = （1）。 8. The method for analyzing right-turn vehicle gap selection behavior based on an electric bicycle according to claim 1, characterized in that: Formula (2) represents the cumulative Weibull distribution function with two parameters, the scale parameter α and the shape parameter β; （2）。 9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for analyzing the right-turn vehicle gap selection behavior based on an electric bicycle according to any one of claims 1 to 8 are implemented.

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